Rengo AI is building the intelligence layer for fund management — starting with next-generation portfolio monitoring systems for investment teams. Today, portfolio monitoring is fragmented across dashboards, spreadsheets, internal tools, and manual analyst workflows. Rengo replaces this with an AI-native monitoring layer that continuously interprets portfolio activity, risk, exposure, and performance across assets and strategies. The Role As a Founding AI Engineer, you will build the core system that powers AI-driven portfolio monitoring for institutional investors. You will design systems that continuously: • ingest portfolio + market + position-level data • detect meaningful changes and anomalies • generate structured investment insights • explain performance and risk drivers in natural language + structured outputs This is a high-reliability AI system, not a chatbot. What You’ll Build 1. AI Portfolio Monitoring Engine • Real-time and batch systems that monitor: • portfolio performance (PnL, attribution, drawdowns) • exposure shifts (sector, geography, asset class) • risk signals (volatility, correlation, concentration) • position-level changes • AI layer that converts raw portfolio data into: • alerts • summaries • explanations • actionable insights 2. Change Detection & Intelligence Layer • Build systems that detect: • significant portfolio movements • abnormal price/volume behavior in holdings • drift from target allocations • risk regime changes • Prioritization layer: what matters vs noise 3. AI-Generated Portfolio Narratives • Generate structured outputs such as: • daily / weekly portfolio reports • performance explanations (“why did we lose/gain?”) • exposure breakdowns • risk commentary • Ensure outputs are: • auditable • grounded in data • consistent across runs 4. Data + Retrieval Systems for Funds • Integrate: • positions & holdings data • market data feeds • internal fund metadata • external news & filings (optional enrichment layer) • Build RAG pipelines over portfolio + market context 5. LLM Systems for Financial Reliability • Design LLM pipelines that: • avoid hallucinated financial reasoning • produce structured, verifiable outputs • ground insights in actual portfolio data • Build evaluation frameworks for correctness of financial narratives Strong engineering background • 3–7+ years in backend, data engineering, or ML systems • Strong Python (mandatory) • Experience building production data systems or analytics platforms LLM / AI systems experience • Experience building LLM applications in production • Strong understanding of: • RAG systems • structured generation (schemas, JSON outputs) • tool use / function calling • agent workflows • Awareness of failure modes in LLM reasoning (critical in finance) Data-heavy systems mindset • Experience with: • time-series data • event-driven pipelines • analytics / observability systems • Comfort working with imperfect, high-volume financial data Nice to Have • Experience in: • asset management / hedge funds / fintech • portfolio analytics or risk systems • trading / market data infrastructure • Familiarity with: • exposure/risk models • PnL attribution systems • BI / analytics platforms for finance • Experience with vector databases or hybrid retrieval systems What Makes This Role Unique • You are building the core monitoring brain of a fund • Not dashboards — interpretation + intelligence • Systems you build directly influence investment decisions and risk awareness • High emphasis on: • correctness • traceability • reliability under uncertainty • You own the full stack: data → intelligence → insight delivery Tech Direction • Python (core systems + AI orchestration) • LLM APIs (OpenAI / Anthropic / open-source models) • Postgres + time-series storage • Vector DB for semantic retrieval • Stream/batch processing pipelines • Cloud infrastructure (AWS/GCP) Why Join • Define how AI monitors institutional portfolios • Replace manual analyst workflows with automated intelligence systems • Work on one of the hardest AI problems in finance: turning data into trustworthy interpretation • High ownership, early-stage, no legacy constraints